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        The quality and maturity level of agricultural products is often associated with their color. For example, in fresh produce markets such as red delicious apples and peaches, dark red represents higher quality than light red. Another example is to use color grading to determine time to market. In general, tomatoes must be harvested while still green. After harvesting, the fruit continue to ripen and their color turns lighter green, then pink, and eventually red. Due to transportation delays, tomatoes that are already ripe (red) when picked must be sold to local markets. Green tomatoes can be shipped to customers over much greater distances.

            Manually separating fruits or vegetables into different color categories is a labor intensive task and its accuracy is not reliable. Most existing color grading systems are not user-friendly and do not allow the operator to adjust grading parameters according to his/her color perception and color preference. We have developed a novel and robust color mapping technique for automated color grading that is well suited for commercial production. The proposed method makes it easy for a human operator to specify and adjust color-preference settings for different color groups representing distinct quality grades.

         The performance of this novel color mapping method is illustrated using tomato maturity evaluation as an example application.  A machine vision system for processing automation has been designed and installed for commercial production.  Its color grading accuracy has been well received.  This method can be easily adapted for other food and fruit processing applications.  Future improvement includes automated color system calibration and more advanced color distribution analysis. We also applied this technique to lip shape detection and analysis application.

 Project Sponsors:

Datepac, LLC, Yuma, Arizona


Dr.  Dong Zhang, Sun Yat-Sen University, Guangzhou, China
Dr.  Guangming Xiong, Beijing Institute of Technology, Beijing, China

 Graduate Students:

Christopher Greco

  1. D. Zhang, D.J. Lee, B.J. Tippetts, and K.D. Lillywhite, “Date Maturity and Quality Evaluation Using Color Distribution Analysis and Back Projection," Journal of Food Engineering, vol. 131, p. 161-169, June 2014.
  2. D. Zhang, D.J. Lee, and A. Desai, “Color Back Projection for Date Maturity Evaluation," SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, vol. 9025-34, San Francisco, CA, USA, February 2-6, 2014.
  3. D.J. Lee, J.K Archibald, and G.M. Xiong, “Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping,” IEEE Transactions on Automation Science and Engineering, vol. 8/2, p. 292-302, April 2011.
  4. D.J. Lee, and J.K Archibald, “Color Image Processing for Date Quality Evaluation,” SPIE Electronic Imaging, Intelligent Robots and Computer Vision XXVII: Algorithms and Techniques, vol. 7539, 75390V1-12, San Jose, CA, USA, January 17-21, 2010.
  5. D.J. Lee, J.K. Archibald, Y.C. Chang, and C.R. Greco, “Robust Color Space Conversion and Color Distribution Analysis Techniques for Date Maturity Evaluation,” Journal of Food Engineering, vol. 88/3, p. 364-372, October 2008.
  6. D.J. Lee, Y.C. Chang, J.K. Archibald, and C.R. Greco, “Color Quantization and Image Analysis for Automated Fruit Quality Evaluation,” IEEE Conference on Automation Science and Engineering (CASE), p. 194-199, Washington DC, USA, August 23-26, 2008.
  7. D.J. Lee, "Color Space Conversion for Linear Color Grading", Proceedings of SPIE Intelligent Robots and Computer Vision XIX, vol. 4197, p. 358-366, Boston, MA, USA, November 2000.

  8. D.J. Lee and R. Anbalagan, "High-speed Automated Color Sorting Vision System,” SPIE Optical Engineering Midwest 1995, vol. 2622, p. 573-579, Chicago, IL, USA, April 1995.
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